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Proceedings Paper

Fuzzy image segmentation for lung nodule detection
Author(s): Yue Shen; Ravi T. Sankar; Wei Qian; Xuejun Sun; Dansheng Song
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Paper Abstract

This paper focuses on evaluating three fuzzy image segmentation algorithms in lung nodule detection scenario: fuzzy entropy-based method, multivariate fuzzy C-means method (MFCM), adaptive fuzzy C-means method (AFCM) and comparing them with the iterative threshold selection method. The experimental result shows that all three methods outperform iterative threshold selection method. The two fuzzy C-means clustering based algorithms achieve better segmentation performance without losing true positives. However, fuzzy entropy-based image segmentation removes the false positives at the cost of losing some true positives, which is a risky approach and hence it is not recommended for lung nodule detection. Moreover, although AFCM outperforms MFCM in true positive detection significantly, in the sense of TPR/FP, MFCM is comparable to AFCM in the confidence interval of significant level 0.95, since AFCM brings in more false positives than MFCM.

Paper Details

Date Published: 30 December 2003
PDF: 8 pages
Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.498263
Show Author Affiliations
Yue Shen, MiraMedica Inc. (United States)
Ravi T. Sankar, Univ. of South Florida (United States)
Wei Qian, Univ. of South Florida (United States)
Xuejun Sun, Univ. of South Florida (United States)
Dansheng Song, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 5200:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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